Data update
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Ingy döt Net
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7735 changed files with 38060 additions and 199180 deletions
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@ -1,23 +0,0 @@
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with Ada.Numerics; use Ada.Numerics;
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with Ada.Numerics.Float_Random; use Ada.Numerics.Float_Random;
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with Ada.Numerics.Elementary_Functions; use Ada.Numerics.Elementary_Functions;
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procedure Normal_Random is
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function Normal_Distribution
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( Seed : Generator;
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Mu : Float := 1.0;
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Sigma : Float := 0.5
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) return Float is
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begin
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return
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Mu + (Sigma * Sqrt (-2.0 * Log (Random (Seed), 10.0)) * Cos (2.0 * Pi * Random (Seed)));
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end Normal_Distribution;
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Seed : Generator;
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Distribution : array (1..1_000) of Float;
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begin
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Reset (Seed);
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for I in Distribution'Range loop
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Distribution (I) := Normal_Distribution (Seed);
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end loop;
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end Normal_Random;
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@ -1,67 +0,0 @@
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IDENTIFICATION DIVISION.
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PROGRAM-ID. RANDOM.
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AUTHOR. Bill Gunshannon
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INSTALLATION. Home.
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DATE-WRITTEN. 14 January 2022.
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************************************************************
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** Program Abstract:
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** Able to get the Mean to be really close to 1.0 but
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** couldn't get the Standard Deviation any closer than
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** .3 to .4.
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************************************************************
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DATA DIVISION.
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WORKING-STORAGE SECTION.
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01 Sample-Size PIC 9(5) VALUE 1000.
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01 Total PIC 9(10)V9(5) VALUE 0.0.
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01 Arith-Mean PIC 999V999 VALUE 0.0.
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01 Std-Dev PIC 999V999 VALUE 0.0.
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01 Seed PIC 999V999.
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01 TI PIC 9(8).
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01 Idx PIC 99999 VALUE 0.
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01 Intermediate PIC 9(10)V9(5) VALUE 0.0.
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01 Rnd-Work.
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05 Rnd-Tbl
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OCCURS 1 TO 99999 TIMES DEPENDING ON Sample-Size.
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10 Rnd PIC 9V9999999 VALUE 0.0.
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PROCEDURE DIVISION.
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Main-Program.
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ACCEPT TI FROM TIME.
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MOVE FUNCTION RANDOM(TI) TO Seed.
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PERFORM WITH TEST AFTER VARYING Idx
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FROM 1 BY 1
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UNTIL Idx = Sample-Size
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COMPUTE Intermediate =
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(FUNCTION RANDOM() * 2.01)
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MOVE Intermediate TO Rnd(Idx)
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END-PERFORM.
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PERFORM WITH TEST AFTER VARYING Idx
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FROM 1 BY 1
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UNTIL Idx = Sample-Size
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COMPUTE Total = Total + Rnd(Idx)
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END-PERFORM.
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COMPUTE Arith-Mean = Total / Sample-Size.
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DISPLAY "Mean: " Arith-Mean.
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PERFORM WITH TEST AFTER VARYING Idx
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FROM 1 BY 1
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UNTIL Idx = Sample-Size
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COMPUTE Intermediate =
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Intermediate + (Rnd(Idx) - Arith-Mean) ** 2
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END-PERFORM.
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COMPUTE Std-Dev = Intermediate / Sample-Size.
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DISPLAY "Std-Dev: " Std-Dev.
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STOP RUN.
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END PROGRAM RANDOM.
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@ -1,7 +1,9 @@
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func logn n .
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return log n 0
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.
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numfmt 0 5
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e = 2.7182818284590452354
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for i = 1 to 1000
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a[] &= 1 + 0.5 * sqrt (-2 * log10 randomf / log10 e) * cos (360 * randomf)
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a[] &= 1 + 0.5 * sqrt (-2 * logn randomf) * cos (360 * randomf)
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.
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for v in a[]
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avg += v / len a[]
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@ -3,8 +3,8 @@ import extensions'math;
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randomNormal()
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{
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^ cos(2 * Pi_value * randomGenerator.nextReal())
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* sqrt(-2 * ln(randomGenerator.nextReal()))
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^ cos(2 * Pi_value * Random.nextReal())
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* sqrt(-2 * ln(Random.nextReal()))
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}
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public program()
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@ -19,7 +19,7 @@ public program()
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};
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tAvg /= a.Length;
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console.printLine("Average: ", tAvg);
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Console.printLine("Average: ", tAvg);
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real s := 0;
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for (int x := 0; x < a.Length; x += 1)
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@ -29,7 +29,7 @@ public program()
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s := sqrt(s / 1000);
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console.printLine("Standard Deviation: ", s);
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Console.printLine("Standard Deviation: ", s);
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console.readChar()
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Console.readChar()
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}
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@ -1,15 +0,0 @@
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include misc.e
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function RandomNormal()
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atom x1, x2
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x1 = rand(999999) / 1000000
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x2 = rand(999999) / 1000000
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return sqrt(-2*log(x1)) * cos(2*PI*x2)
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end function
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constant n = 1000
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sequence s
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s = repeat(0,n)
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for i = 1 to n do
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s[i] = 1 + 0.5 * RandomNormal()
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end for
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@ -1,10 +1,12 @@
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-->
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<span style="color: #008080;">function</span> <span style="color: #000000;">RandomNormal</span><span style="color: #0000FF;">()</span>
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<span style="color: #008080;">return</span> <span style="color: #7060A8;">sqrt</span><span style="color: #0000FF;">(-</span><span style="color: #000000;">2</span><span style="color: #0000FF;">*</span><span style="color: #7060A8;">log</span><span style="color: #0000FF;">(</span><span style="color: #7060A8;">rnd</span><span style="color: #0000FF;">()))</span> <span style="color: #0000FF;">*</span> <span style="color: #7060A8;">cos</span><span style="color: #0000FF;">(</span><span style="color: #000000;">2</span><span style="color: #0000FF;">*</span><span style="color: #000000;">PI</span><span style="color: #0000FF;">*</span><span style="color: #7060A8;">rnd</span><span style="color: #0000FF;">())</span>
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<span style="color: #008080;">end</span> <span style="color: #008080;">function</span>
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with javascript_semantics
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requires("1.0.6") -- std_dev
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function random_normal()
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return sqrt(-2*log(rnd())) * cos(2*PI*rnd())
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end function
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<span style="color: #004080;">sequence</span> <span style="color: #000000;">s</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">repeat</span><span style="color: #0000FF;">(</span><span style="color: #000000;">0</span><span style="color: #0000FF;">,</span><span style="color: #000000;">1000</span><span style="color: #0000FF;">)</span>
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<span style="color: #008080;">for</span> <span style="color: #000000;">i</span><span style="color: #0000FF;">=</span><span style="color: #000000;">1</span> <span style="color: #008080;">to</span> <span style="color: #7060A8;">length</span><span style="color: #0000FF;">(</span><span style="color: #000000;">s</span><span style="color: #0000FF;">)</span> <span style="color: #008080;">do</span>
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<span style="color: #000000;">s</span><span style="color: #0000FF;">[</span><span style="color: #000000;">i</span><span style="color: #0000FF;">]</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">1</span> <span style="color: #0000FF;">+</span> <span style="color: #000000;">0.5</span> <span style="color: #0000FF;">*</span> <span style="color: #000000;">RandomNormal</span><span style="color: #0000FF;">()</span>
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<span style="color: #008080;">end</span> <span style="color: #008080;">for</span>
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<!--
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sequence s = repeat(0,1000)
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for i=1 to length(s) do
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s[i] = 1 + 0.5 * random_normal()
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end for
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printf(1,"mean: %g, std dev: %g\n",{average(s),std_dev(s)})
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@ -1,35 +0,0 @@
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function Get-RandomNormal
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{
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[CmdletBinding()]
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Param ( [double]$Mean, [double]$StandardDeviation )
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$RandomNormal = $Mean + $StandardDeviation * [math]::Sqrt( -2 * [math]::Log( ( Get-Random -Minimum 0.0 -Maximum 1.0 ) ) ) * [math]::Cos( 2 * [math]::PI * ( Get-Random -Minimum 0.0 -Maximum 1.0 ) )
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return $RandomNormal
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}
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# Standard deviation function for testing
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function Get-StandardDeviation
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{
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[CmdletBinding()]
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param ( [double[]]$Numbers )
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$Measure = $Numbers | Measure-Object -Average
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$PopulationDeviation = 0
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ForEach ($Number in $Numbers) { $PopulationDeviation += [math]::Pow( ( $Number - $Measure.Average ), 2 ) }
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$StandardDeviation = [math]::Sqrt( $PopulationDeviation / ( $Measure.Count - 1 ) )
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return $StandardDeviation
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}
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# Test
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$RandomNormalNumbers = 1..1000 | ForEach { Get-RandomNormal -Mean 1 -StandardDeviation 0.5 }
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$Measure = $RandomNormalNumbers | Measure-Object -Average
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$Stats = [PSCustomObject]@{
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Count = $Measure.Count
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Average = $Measure.Average
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StandardDeviation = Get-StandardDeviation -Numbers $RandomNormalNumbers
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}
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$Stats | Format-List
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@ -1,5 +1,5 @@
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-- 28 Jul 2025
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include Settings
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-- 24 Aug 2025
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include Setting
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say 'RANDOM NUMBERS'
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say version
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let pi = 4.0 *. atan(1.0)
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let random_gaussian = () => {
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1.0 +.
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sqrt(-2.0 *. log(Random.float(1.0))) *.
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cos(2.0 *. pi *. Random.float(1.0))
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}
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let a = Belt.Array.makeBy(1000, (_) => random_gaussian ())
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for i in 0 to 10 {
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Js.log(a[i])
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}
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